Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement
The Swiss State Secretariat for Migration recently announced a pilot project for a machine learning-based assignment process for refugee resettlement. This approach has the potential to substantially increase the overall employment rate of refugees in Switzerland. However, the currently proposed met...
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| creator | Olberg, Nils Seuken, Sven |
| description | The Swiss State Secretariat for Migration recently announced a pilot project
for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees. |
| doi_str_mv | 10.48550/arxiv.2203.16176 |
| format | Article |
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for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees.</description><identifier>DOI: 10.48550/arxiv.2203.16176</identifier><language>eng</language><subject>Computer Science - Computer Science and Game Theory</subject><creationdate>2022-03</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2203.16176$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2203.16176$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Olberg, Nils</creatorcontrib><creatorcontrib>Seuken, Sven</creatorcontrib><title>Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement</title><description>The Swiss State Secretariat for Migration recently announced a pilot project
for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees.</description><subject>Computer Science - Computer Science and Game Theory</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8FOwzAQRH3pARU-gBP-AYeNnWycI6paQAqqhHKPNmQdLKVu5YQK_h63cNlZzUijeULc55AVtizhkeK3P2dag8lyzCu8EfttoH7yYZRtpIHV0blZ-iDf6OPTB5YNUwwpVj3NPCR7ufijdMco39l9jcxJZ16WiQ8clluxcjTNfPeva9Hutu3mRTX759fNU6MIK1Sm144tcFFbZCptWaWXLNZVbrRDIjI1AJB1FkEj2zpdxwQwFLZP89fi4a_2StSdoj9Q_OkuZN2VzPwCdQRIIA</recordid><startdate>20220330</startdate><enddate>20220330</enddate><creator>Olberg, Nils</creator><creator>Seuken, Sven</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220330</creationdate><title>Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement</title><author>Olberg, Nils ; Seuken, Sven</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-3b2fe80e4986ea5857e49a8697132f6aaa39000a8f86026e89026fea00d48b203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computer Science and Game Theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Olberg, Nils</creatorcontrib><creatorcontrib>Seuken, Sven</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Olberg, Nils</au><au>Seuken, Sven</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement</atitle><date>2022-03-30</date><risdate>2022</risdate><abstract>The Swiss State Secretariat for Migration recently announced a pilot project
for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees.</abstract><doi>10.48550/arxiv.2203.16176</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Science and Game Theory |
| title | Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement |
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